Enterprise Tax Systems Modernization Using Intelligent Integration Frameworks

Abstract

Corporate tax departments continue to operate on spreadsheets and manual review cycles even as adjacent finance domains have modernized around real-time data and machine learning. This paper introduces the Intelligent Tax Integration Framework (ITIF), a six-layer domain-specific architecture for artificial intelligence (AI)-driven enterprise tax systems modernization. ITIF addresses six interlocking challenges: fragmented multi-jurisdiction data, tax classification at the stock-keeping unit (SKU) scale, real-time transfer pricing (TP) monitoring, indirect tax determination for ambiguous categories, current and deferred tax provision under Accounting Standards Codification (ASC) 740 and International Accounting Standard (IAS) 12, and filing readiness across an expanding patchwork of digital reporting regimes. Each layer pairs a specific AI technique—fine-tuned Bidirectional Encoder Representations from Transformers (BERT), an Isolation Forest and long short-term memory (LSTM) ensemble with Shapley additive explanations (SHAP) attribution, Monte Carlo simulation for uncertain tax positions, and Retrieval-Augmented Generation (RAG) for audit queries—with regulatory knowledge graphs anchored to Organization for Economic Co-operation and Development (OECD), Financial Accounting Standards Board (FASB), and International Accounting Standards Board (IASB) source material. A simulated case study illustrates deployment across eighteen legal entities and twelve jurisdictions, grounded in practitioner experience from a Systems, Applications, and Products (SAP) to Microsoft Dynamics AX migration at a high-volume global technology manufacturer. A nine-dimension comparative evaluation positions ITIF against legacy tax systems and rule-based tax engines and identifies continuous tax intelligence coverage during enterprise resource planning (ERP) cutover as the framework’s most distinctive capability.